Patients with ACPA-positive and ACPA-negative rheumatoid arthritis show different serological autoantibody repertoires and autoantibody associations with disease activity

Patients with rheumatoid arthritis (RA) can test either positive or negative for circulating anti-citrullinated protein antibodies (ACPA) and are thereby categorized as ACPA-positive (ACPA+) or ACPA-negative (ACPA−), respectively. In this study, we aimed to elucidate a broader range of serological autoantibodies that could further explain immunological differences between patients with ACPA+ RA and ACPA− RA. On serum collected from adult patients with ACPA+ RA (n = 32), ACPA− RA (n = 30), and matched healthy controls (n = 30), we used a highly multiplex autoantibody profiling assay to screen for over 1600 IgG autoantibodies that target full-length, correctly folded, native human proteins. We identified differences in serum autoantibodies between patients with ACPA+ RA and ACPA− RA compared with healthy controls. Specifically, we found 22 and 19 autoantibodies with significantly higher abundances in ACPA+ RA patients and ACPA− RA patients, respectively. Among these two sets of autoantibodies, only one autoantibody (anti-GTF2A2) was common in both comparisons; this provides further evidence of immunological differences between these two RA subgroups despite sharing similar symptoms. On the other hand, we identified 30 and 25 autoantibodies with lower abundances in ACPA+ RA and ACPA− RA, respectively, of which 8 autoantibodies were common in both comparisons; we report for the first time that the depletion of certain autoantibodies may be linked to this autoimmune disease. Functional enrichment analysis of the protein antigens targeted by these autoantibodies showed an over-representation of a range of essential biological processes, including programmed cell death, metabolism, and signal transduction. Lastly, we found that autoantibodies correlate with Clinical Disease Activity Index, but associate differently depending on patients’ ACPA status. In all, we present candidate autoantibody biomarker signatures associated with ACPA status and disease activity in RA, providing a promising avenue for patient stratification and diagnostics.


Microarray data handling and pre-processing
For each slide, proteins and control probes are in quadruplicates, with 4 arrays on each slide.
Step 1: Calculate the net intensities for each spot by subtracting the background signal intensities from the foreground signal intensities of each spot. For each spot, the background signal intensity is calculated using a circular region that is centered on the spot. The figure below is a visual representation of how this calculation was carried out (Molecular Devices). The red spot in the middle is the spot of interest.
Step 3: Zero net intensities if only 1 replica spot remaining.
Step 4: Calculate the percentage of coefficient of variant (CV%) to determine the variations between the replica spots on each slide.

× 100%
Flag a set of replica spots with only 2 or less replica/s remaining and CV% > 20% as "High CV". The mean RFU of these replica spots (proteins) will be excluded from the downstream analysis.
For the proteins and controls with a CV% > 20% and with 3 or more replica spots remaining, the replica spots that resulted in this high CV% value were filtered out. This was done by first calculating the standard deviation between the median value of the net intensities and the individual net intensities for each set of the replica spots. The spot with the largest standard deviation was removed. CV% values were re-calculated and the process repeated.
Step 5: Calculate the mean of the net intensities for the remaining replica spots.
Step 6: Inspect the signal intensities of the two positive controls: IgG and Cy3-BSA.
Step 7: Composite normalization of data using both quantile-based and total intensity-based modules (Duarte, J. et al., 2013). This method assumes that the different samples share a common distribution of their control probes while taking into account the potential existence of flagged spots within them. The Immunome array uses Cy3labeled biotinylated BSA (Cy3-BSA) replicates as the positive control spots across slides. It is considered a housekeeping probe for normalization of signal intensities for any given study.
The quantile module adopts the algorithm in Bolstad et al., 2003. This enables the detection and handling of outliers or flagged spots in any of the Cy3-BSA control probes. A total intensity-based module was then implemented to obtain a scaling factor for each sample. This assumes that post-normalization, the positive controls should have a common total intensity value across all samples (Causton H.C. et al., 2004). This method aims to normalize the protein array data from variations in their measurements while preserving the targeted biological activity across samples.

Quantile-based normalization of all Cy3-BSA across all samples
(i = spot number and j = sample number) 1. Load all Cy3-BSA across all samples, j, into an i  j matrix X 2. Sort spot intensities in each column j of X to get Xsort 3. Take the mean across each row i of Xsort to get < Xi >

Intensity-based normalization
1. Calculate the sum of the mean across each row i, ∑ < X i >i 2. For each sample, k, calculate the sum of all Cy3-BSA controls, ∑ Xk 3. For each sample, k, Scaling factor(k) = ∑<Xi> ∑ Xk